Not as a vendor. As practitioners who have built these systems, debugged them in production, and had to explain them to underwriters and regulators.
Every downstream decision an agent makes inherits the error that entered upstream. Trust in AI outcomes is not a function of the model. It's a function of the data pipeline. Carriers who treat data certification as a pre-deployment step, not a foundation, will keep hitting the same wall.
When an AI agent participates in an underwriting decision or claims adjudication, regulators apply the same explainability and audit requirements as they do to a human adjuster. You can't build a governed outcome on an ungoverned pipeline. The architecture has to be designed for accountability from the start.
Your appetite rules, jurisdiction requirements, and SOPs should be encoded, versioned, and applied consistently, not recalled probabilistically from a model that may have drifted since last training. Every AI decision should trace back to your procedures, your expertise, your institutional knowledge.
Proof-of-value expectations have shifted from "interesting" to "in production." Scaling agentic AI at this stage requires a method, not just a model: one that certifies the data before agents consume it, contextualizes institutional knowledge so agents reason correctly, and composes workflows that are auditable end to end.
Bad data in an agentic workflow doesn't fail at the source. It compounds across every downstream decision. Every data product needs a trust score before an agent touches it.
Appetite, jurisdiction rules, and SOPs as versioned, governed definitions, not floating in model weights where they can drift, hallucinate, or become unauditable.
Underwriting and claims AI faces the same audit standards as human decisions. Explainability has to be designed in. It cannot be retrofitted onto a black-box pipeline.
The governed intelligence stack behind the use cases — from AI-ready data pipelines to traceable agentic workflows.